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Modern web applications consist of a significant amount of clientside code, written in JavaScript, HTML, and CSS. In this paper, we present a study of common challenges and misconceptions among web developers, by mining related questions asked on Stack Overflow. We use unsupervised learning to categorize the mined questions and define a ranking algorithm to rank all the Stack Overflow questions based on their importance. We analyze the top 50 questions qualitatively. The results indicate that (1) the overall share of web development related discussions is increasing among developers, (2) browser related discussions are prevalent; however, this share is decreasing with time, (3) form validation and other DOM related discussions have been discussed consistently over time, (4) web related discussions are becoming more prevalent in mobile development, and (5) developers face implementation issues with new HTML5 features such as Canvas. We examine the implications of the results on the development, research, and standardization communities.

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Abstract-While graphics processing units (GPUs) have gained wide adoption as accelerators for general-purpose applications (GPGPU), the end-to-end reliability implications of their use have not been quantified. Fault injection is a widely used method for evaluating the reliability of applications. However, building a fault injector for GPGPU applications is challenging due to their massive parallelism, which makes it difficult to achieve representativeness while being time-efficient. This paper makes three key contributions. First, it presents the design of a fault-injection methodology to evaluate end-to-end reliability properties of application kernels running on GPUs. Second, it introduces a fault-injection tool that uses real GPU hardware and offers a good balance between the representativeness and the efficiency of the fault injection experiments. Third, this paper characterizes the error resilience characteristics of twelve GPGPU applications.

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Mutation testing is an effective test adequacy assessment technique. However, it suffers from two main issues. First, there is a high computational cost in executing the test suite against a potentially large pool of generated mutants. Second, there is much effort involved in filtering out equivalent mutants, which are syntactically different but semantically identical to the original program. Prior work has mainly focused on detecting equivalent mutants after the mutation generation phase, which is computationally expensive and has limited efficiency. In this paper, we propose a technique that leverages static and dynamic program analysis to guide the mutation generation process a-priori towards parts of the code that are error-prone or likely to influence the program's output. Further, we focus on the JavaScript language, and propose a set of mutation operators that are specific to web applications. We implement our approach in a tool called MUTANDIS. We empirically evaluate MUTANDIS on a number of web applications to assess the efficacy of the approach.